File size: 5,735 Bytes
a35b997 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 |
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Thyroid_Cancer"
cohort = "GSE138198"
# Input paths
in_trait_dir = "../DATA/GEO/Thyroid_Cancer"
in_cohort_dir = "../DATA/GEO/Thyroid_Cancer/GSE138198"
# Output paths
out_data_file = "./output/preprocess/3/Thyroid_Cancer/GSE138198.csv"
out_gene_data_file = "./output/preprocess/3/Thyroid_Cancer/gene_data/GSE138198.csv"
out_clinical_data_file = "./output/preprocess/3/Thyroid_Cancer/clinical_data/GSE138198.csv"
json_path = "./output/preprocess/3/Thyroid_Cancer/cohort_info.json"
# Get file paths for SOFT and matrix files
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
# Get background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
# Create dictionary of unique values for each feature
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print the information
print("Dataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in unique_values_dict.items():
print(f"\n{feature}:")
print(values)
# 1. Gene Expression Data Availability
# Dataset uses Affymetrix Human Gene 1.0 ST arrays, which measures gene expression
is_gene_available = True
# 2.1 Data Availability and Row Identification
trait_row = 1 # 'sample type' contains cancer vs normal info
gender_row = 0 # Gender is available
age_row = None # Age is not available
# 2.2 Data Type Conversion Functions
def convert_trait(value):
if not value or ":" not in value:
return None
value = value.split(": ")[1].lower()
# Convert to binary: 1 for cancer (PTC), 0 for normal
if "normal" in value:
return 0
elif "ptc" in value or "papillary thyroid carcinoma" in value:
return 1
return None
def convert_gender(value):
if not value or ":" not in value:
return None
value = value.split(": ")[1].lower()
if value == "f":
return 0
elif value == "m":
return 1
return None
# 3. Save Metadata - Initial Filtering
validate_and_save_cohort_info(
is_final=False,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=trait_row is not None
)
# 4. Clinical Feature Extraction
if trait_row is not None:
selected_clinical = geo_select_clinical_features(
clinical_df=clinical_data,
trait=trait,
trait_row=trait_row,
convert_trait=convert_trait,
gender_row=gender_row,
convert_gender=convert_gender
)
# Preview the processed clinical data
print("Preview of processed clinical data:")
print(preview_df(selected_clinical))
# Save clinical data
selected_clinical.to_csv(out_clinical_data_file)
# Extract genetic data matrix
genetic_data = get_genetic_data(matrix_file_path)
# Print first few rows with column names to examine data structure
print("Data preview:")
print("\nColumn names:")
print(list(genetic_data.columns)[:5])
print("\nFirst 5 rows:")
print(genetic_data.head())
print("\nShape:", genetic_data.shape)
# Verify this is gene expression data and check identifiers
is_gene_available = True
# Save updated metadata
validate_and_save_cohort_info(
is_final=False,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=(trait_row is not None)
)
# Save gene expression data
genetic_data.to_csv(out_gene_data_file)
# The identifiers in the index appear to be numeric IDs (e.g. 7892501, 7892502)
# These are not standard human gene symbols (which are usually alphanumeric like BRCA1, TP53)
# Therefore, a mapping from probe IDs to gene symbols will be required
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file_path)
# Preview column names and first few values
preview = preview_df(gene_metadata)
print("\nGene annotation columns and sample values:")
print(preview)
# Extract probe-to-gene mapping from annotation data
# The 'ID' column matches probe IDs in expression data
# 'gene_assignment' contains gene symbols in a specific format
mapping_df = get_gene_mapping(gene_metadata, 'ID', 'gene_assignment')
# Convert probe-level data to gene-level data using the mapping
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# Preview gene expression data
print("\nGene expression data preview:")
print("Shape:", gene_data.shape)
print("\nFirst few rows:")
print(gene_data.head())
# 1. Normalize gene symbols and save gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# 4. Judge bias in features and remove biased ones
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Final validation and save metadata
is_usable = validate_and_save_cohort_info(
is_final=True,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=True,
is_biased=trait_biased,
df=linked_data,
note="Dataset contains gene expression data comparing 27 follicular thyroid cancers with 25 follicular thyroid adenomas."
)
# 6. Save linked data if usable
if is_usable:
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
linked_data.to_csv(out_data_file) |